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The dynamic spectrum of microRNA (miRNA) has grown significantly over the years with its identification and exploration in cancer therapeutics and is currently identified as an important resource for innovative strategies due to its functional behavior for gene regulation and modulation of complex biological networks. The progression of cancer is the consequence of uncontrolled, nonsynchronous procedural faults in the biological system. Diversified and variable cellular response of cancerous cells has always raised challenges in effective cancer therapy. miRNAs, a class of non-coding RNAs (ncRNAs), are the natural genetic gift, responsible to preserve the homeostasis of cell to nurture. The unprecedented significance of endogenous miRNAs has exhibited promising therapeutic potential in cancer therapeutics. Currently, miRNA mimic miR-34, and an antimiR aimed against miR-122 has entered the clinical trials for cancer treatments. This review, highlights the recent breakthroughs, challenges, clinical trials, and advanced delivery vehicles in the administration of miRNA therapies for precision oncology.

Post-transcriptional regulation in multicellular organisms is mediated by microRNAs. However, the principles that determine if a gene is regulated by miRNAs are poorly understood. Previous works focused mostly on miRNA seed matches and other features of the 3′-UTR of transcripts. These common approaches rely on knowledge of the miRNA families, and computational approaches still yield poor, inconsistent results, with many false positives. In this work, we present a different paradigm for predicting miRNA-regulated genes based on the encoded proteins. In a novel, automated machine learning framework, we use sequence as well as diverse functional annotations to train models on multiple organisms using experimentally validated data. We present insights from tens of millions of features extracted and ranked from different modalities. We show high predictive performance per organism and in generalization across species. We provide a list of novel predictions including Danio rerio (zebrafish) and Arabidopsis thaliana (mouse-ear cress). We compare genomic models and observe that our protein model outperforms, whereas a unified model improves on both. While most membranous and disease related proteins are regulated by miRNAs, the G-protein coupled receptor (GPCR) family is an exception, being mostly unregulated by miRNAs. We further show that the evolutionary conservation among paralogs does not imply any coherence in miRNA regulation. We conclude that duplicated paralogous genes that often changed their function, also diverse in their tendency to be miRNA regulated. We conclude that protein function is informative across species in predicting post-transcriptional miRNA regulation in living cells.

Sialic acids and MMPs play critical roles in inflammatory diseases. Furthermore, Interaction between Sialic acid and receptors such as siglecs leads phosphorylation of ITIM domains and promote downstream inhibitory signaling through SHP-1 phosphatases. SHP1 could positively regulate TNF-α and by control, the production of TNF-α could play a crucial role in inflammation. Besides, TNF-α could mediate the signaling pathway leading to MMP-9 gene expression. MMP-9 also is recognized as therapeutic targets in a variety of diseases including vascular pathologies, cancers, and auto-immuned diseases. The present in-silico study aims to identify the most potent micro-RNAs could control the signaling pathway from siglec to MMP-9. To this end, with review some articles and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis 21 genes involved in this pathway have been selected. Then TARGET SCAN, DIANA-TarBase8, and miRDB database were utilized to predict the miRNAs which have the most effective to target genes. Finally, using bio-studying Software Cytoscape, three microRNA-mRNA networks were constructed for existing banks. We found shared micro-RNAs that in the three networks. Eventually, using miRTarBase database microRNAs that were linked to more genes in this path were assigned a higher privilege. The twenty-one selected micro-RNAs could be the proper options for experimental studies from sialic acid receptors (siglecs) to MMP-9. Among them miR-34a-5p could be the most interesting target.

MicroRNAs (miRNAs), approximately 22 nucleotides long, posttranscriptionally active gene expression regulators, play active roles in modulating cellular processes. Gene regulation and miRNA regulation are intertwined and the main aim of this study is to facilitate the analysis of miRNAs within gene regulatory pathways. VANESA enables the reconstruction of biological pathways and supports visualization and simulation. To support integrative miRNA and gene pathway analyses, a custom database of experimentally proven miRNAs, integrating data from miRBase, TarBase and miRTarBase, was added to DAWIS-M.D., which is the main data source for VANESA. Analysis of human KEGG pathways within DAWIS-M.D. showed that 661 miRNAs (~1/3 recorded human miRNAs) lead to 65,474 interactions. hsa-miR-335-5p targets most genes in our system (2,544); while the most targeted gene (with 71 miRNAs) is NUFIP2 (Nuclear Fragile X Mental Retardation Protein Interacting Protein 2). Amyotrophic Lateral Sclerosis (ALS), a complex neurodegenerative disease, was chosen as a proof of concept model. Using our system, it was possible to reduce the initially several hundred genes and miRNAs associated with ALS to eight genes, 19 miRNAs and 31 interactions. This highlights the effectiveness of the implemented system to distill important information from otherwise hard to access, highly convoluted and vast regulatory networks. [ABSTRACT FROM AUTHOR]

Abstract In the era of post genomics, performing a computational analysis to understand the pharmacogenomic based regulation in Psoriatic Arthritis with respect to the principles of data mining and constructing a regulatory network with respect to the principles of systems biology and analyzing the network with respect to the principles of test statistic remains a challenging task to execute. The challenge was approached by identifying the associated genes of Psoriatic Arthritis from PharmGKB and it was followed by identifying the associated regulators (MicroRNAs and Transcription Factors) from miRTarBase/RegNetworks. Finally the compatibility of miRNA in Regulatory Networks is analyzed by the statistical measure in miRmap. [ABSTRACT FROM AUTHOR]

Abstract: MicroRNAs (miRNAs) are a group of small noncoding RNAs that regulate translational repression of target mRNAs. The vast majority of presently identified miRNAs are expressed in the brain where they fine-tune the expression of a wide range of target molecules essential for neuronal and glial development, differentiation, proliferation, apoptosis and metabolism. Aberrant expression and dysfunction of brain-enriched miRNAs induce development of neurodegenerative diseases, such as Alzheimer''s disease (AD) and Parkinson''s disease (PD). Because a single miRNA concurrently downregulates hundreds of target mRNAs, the set of miRNA target genes coregulated by an individual miRNA generally constitutes the biologically integrated network of functionally associated molecules. Recent advances in systems biology enable us to characterize the global molecular network of experimentally validated targets for individual miRNAs by using pathway analysis tools of bioinformatics endowed with comprehensive knowledgebase. This review is conducted to summarize accumulating studies focused on aberrant miRNA expression in AD brains, and to propose the systems biological view that abnormal regulation of cell cycle progression as a result of deregulation of miRNA target networks plays a central role in the pathogenesis of AD. [Copyright &y& Elsevier]